Development of Lightweight Image and Video Processing Algorithms for Energy – Efficient Object Tracking on Battery Powered Edge Devices

For real time object monitoring, battery powered edge devices – like wearable health monitors, drones and autonomous robots need energy efficient solutions. To overcome these difficulties, this work investigates low power image and video processing techniques that are tailored for hardware – constrained situations. Utilizing strategies like knowledge distribution, and model pruning, the algorithms are made to reduce computational overhead while preserving tracking accuracy. The system is implemented in C/C++ and coupled with frameworks such as TensorFlowLite and OpenCV. It is tested on a variety of use cases such as wearable monitoring, drone surveillance and industrial automation. To meet the exceeding demands for energy efficiency in mobile and IoT applications, this research offers a scalable and sustainable way to improve object tracking performance on AI edge devices.